Here are the classes, structs, unions and interfaces with brief descriptions:

CAlphabet | The class Alphabet implements an alphabet and alphabet utility functions |

CArray< T > | Template class Array implements a dense one dimensional array |

CArray2< T > | Template class Array2 implements a dense two dimensional array |

CArray3< T > | Template class Array3 implements a dense three dimensional array |

CAsciiFile | A Ascii File access class |

CAttributeFeatures | Implements attributed features, that is in the simplest case a number of (attribute, value) pairs |

CAUCKernel | The AUC kernel can be used to maximize the area under the receiver operator characteristic curve (AUC) instead of margin in SVM training |

CAvgDiagKernelNormalizer | Normalize the kernel by either a constant or the average value of the diagonal elements (depending on argument c of the constructor) |

CBinaryFile | A Binary file access class |

CBinaryStream< T > | Memory mapped emulation via binary streams (files) |

CBitString | String class embedding a string in a compact bit representation |

CBrayCurtisDistance | Class Bray-Curtis distance |

CCache< T > | Template class Cache implements a simple cache |

CCanberraMetric | Class CanberraMetric |

CCanberraWordDistance | Class CanberraWordDistance |

CChebyshewMetric | Class ChebyshewMetric |

CChi2Kernel | The Chi2 kernel operating on realvalued vectors computes the chi-squared distance between sets of histograms |

CChiSquareDistance | Class ChiSquareDistance |

CClassifier | A generic classifier interface |

CCombinedDotFeatures | Features that allow stacking of a number of DotFeatures |

CCombinedFeatures | The class CombinedFeatures is used to combine a number of of feature objects into a single CombinedFeatures object |

CCombinedKernel | The Combined kernel is used to combine a number of kernels into a single CombinedKernel object by linear combination |

CCommUlongStringKernel | The CommUlongString kernel may be used to compute the spectrum kernel from strings that have been mapped into unsigned 64bit integers |

CCommWordStringKernel | The CommWordString kernel may be used to compute the spectrum kernel from strings that have been mapped into unsigned 16bit integers |

CCompressor | |

CConstKernel | The Constant Kernel returns a constant for all elements |

CCosineDistance | Class CosineDistance |

CCplex | |

CCPLEXSVM | |

CCustomDistance | The Custom Distance allows for custom user provided distance matrices |

CCustomKernel | The Custom Kernel allows for custom user provided kernel matrices |

CDecompressString< ST > | Preprocessor that decompresses compressed strings |

CDiagKernel | The Diagonal Kernel returns a constant for the diagonal and zero otherwise |

CDiceKernelNormalizer | DiceKernelNormalizer performs kernel normalization inspired by the Dice coefficient (see http://en.wikipedia.org/wiki/Dice's_coefficient) |

CDistance | Class Distance |

CDistanceKernel | The Distance kernel takes a distance as input |

CDistanceMachine | A generic DistanceMachine interface |

CDistribution | Base class Distribution from which all methods implementing a distribution are derived |

CDomainAdaptationSVM | Class DomainAdaptiveSVM |

CDomainAdaptationSVMLinear | Class DomainAdaptiveSVMLinear |

CDotFeatures | Features that support dot products among other operations |

CDotKernel | Template class DotKernel is the base class for kernels working on DotFeatures |

CDummyFeatures | The class DummyFeatures implements features that only know the number of feature objects (but don't actually contain any) |

CDynamicArray< T > | Template Dynamic array class that creates an array that can be used like a list or an array |

CDynamicArrayPtr | Template Dynamic array class that creates an array that can be used like a list or an array |

CDynInt< T, sz > | Integer type of dynamic size |

CDynProg | Dynamic Programming Class |

CEuclidianDistance | Class EuclidianDistance |

CExplicitSpecFeatures | Features that compute the Spectrum Kernel feature space explicitly |

CFeatures | The class Features is the base class of all feature objects |

CFile | A File access base class |

CFirstElementKernelNormalizer | Normalize the kernel by a constant obtained from the first element of the kernel matrix, i.e. |

CFixedDegreeStringKernel | The FixedDegree String kernel takes as input two strings of same size and counts the number of matches of length d |

CFKFeatures | The class FKFeatures implements Fischer kernel features obtained from two Hidden Markov models |

CGaussianKernel | The well known Gaussian kernel (swiss army knife for SVMs) computed on CDotFeatures |

CGaussianMatchStringKernel | The class GaussianMatchStringKernel computes a variant of the Gaussian kernel on strings of same length |

CGaussianShiftKernel | An experimental kernel inspired by the WeightedDegreePositionStringKernel and the Gaussian kernel |

CGaussianShortRealKernel | The well known Gaussian kernel (swiss army knife for SVMs) on dense short-real valued features |

CGCArray< T > | |

CGeodesicMetric | Class GeodesicMetric |

CGHMM | Class GHMM - this class is non-functional and was meant to implement a Generalize Hidden Markov Model (aka Semi Hidden Markov HMM) |

CGMNPLib | Class GMNPLib Library of solvers for Generalized Minimal Norm Problem (GMNP) |

CGMNPSVM | Class GMNPSVM implements a one vs. rest MultiClass SVM |

CGNPPLib | Class GNPPLib, a Library of solvers for Generalized Nearest Point Problem (GNPP) |

CGNPPSVM | Class GNPPSVM |

CGPBTSVM | Class GPBTSVM |

CHammingWordDistance | Class HammingWordDistance |

CHash | Collection of Hashing Functions |

CHashedWDFeatures | Features that compute the Weighted Degreee Kernel feature space explicitly |

CHashedWDFeaturesTransposed | Features that compute the Weighted Degreee Kernel feature space explicitly |

CHierarchical | Agglomerative hierarchical single linkage clustering |

CHistogram | Class Histogram computes a histogram over all 16bit unsigned integers in the features |

CHistogramIntersectionKernel | The HistogramIntersection kernel operating on realvalued vectors computes the histogram intersection distance between sets of histograms. Note: the current implementation assumes positive values for the histograms, and input vectors should sum to 1 |

CHistogramWordStringKernel | The HistogramWordString computes the TOP kernel on inhomogeneous Markov Chains |

CHMM | Hidden Markov Model |

CIdentityKernelNormalizer | Identity Kernel Normalization, i.e. no normalization is applied |

CImplicitWeightedSpecFeatures | Features that compute the Weighted Spectrum Kernel feature space explicitly |

CIndirectObject< T, P > | Array class that accesses elements indirectly via an index array |

CIntronList | Class IntronList |

CJensenMetric | Class JensenMetric |

CKernel | The Kernel base class |

CKernelDistance | The Kernel distance takes a distance as input |

CKernelMachine | A generic KernelMachine interface |

CKernelNormalizer | The class Kernel Normalizer defines a function to post-process kernel values |

CKernelPerceptron | Class KernelPerceptron - currently unfinished implementation of a Kernel Perceptron |

CKMeans | KMeans clustering, partitions the data into k (a-priori specified) clusters |

CKNN | Class KNN, an implementation of the standard k-nearest neigbor classifier |

CKRR | |

CLabels | The class Labels models labels, i.e. class assignments of objects |

CLaRank | |

CLBPPyrDotFeatures | Implement DotFeatures for the polynomial kernel |

CLDA | |

CLibLinear | Class to implement LibLinear |

CLibSVM | LibSVM |

CLibSVMMultiClass | Class LibSVMMultiClass |

CLibSVMOneClass | Class LibSVMOneClass |

CLibSVR | Class LibSVR, performs support vector regression using LibSVM |

CLinearClassifier | Class LinearClassifier is a generic interface for all kinds of linear classifiers |

CLinearHMM | The class LinearHMM is for learning Higher Order Markov chains |

CLinearKernel | Computes the standard linear kernel on CDotFeatures |

CLinearStringKernel | Computes the standard linear kernel on dense char valued features |

CList | Class List implements a doubly connected list for low-level-objects |

CListElement | Class ListElement, defines how an element of the the list looks like |

CLocalAlignmentStringKernel | The LocalAlignmentString kernel compares two sequences through all possible local alignments between the two sequences |

CLocalityImprovedStringKernel | The LocalityImprovedString kernel is inspired by the polynomial kernel. Comparing neighboring characters it puts emphasize on local features |

CLogPlusOne | Preprocessor LogPlusOne does what the name says, it adds one to a dense real valued vector and takes the logarithm of each component of it |

CLPBoost | |

CLPM | |

CManhattanMetric | Class ManhattanMetric |

CManhattanWordDistance | Class ManhattanWordDistance |

CMatchWordStringKernel | The class MatchWordStringKernel computes a variant of the polynomial kernel on strings of same length converted to a word alphabet |

CMath | Class which collects generic mathematical functions |

CMemoryMappedFile< T > | Memory mapped file |

CMinkowskiMetric | Class MinkowskiMetric |

CMKL | Multiple Kernel Learning |

CMKLClassification | Multiple Kernel Learning for two-class-classification |

CMKLMultiClass | MKLMultiClass is a class for L1-norm multiclass MKL |

CMKLOneClass | Multiple Kernel Learning for one-class-classification |

CMKLRegression | Multiple Kernel Learning for regression |

CMPDSVM | Class MPDSVM |

CMultiClassSVM | Class MultiClassSVM |

CMultitaskKernelMaskNormalizer | The MultitaskKernel allows Multitask Learning via a modified kernel function |

CMultitaskKernelMaskPairNormalizer | The MultitaskKernel allows Multitask Learning via a modified kernel function |

CMultitaskKernelMklNormalizer | Base-class for parameterized Kernel Normalizers |

CMultitaskKernelNormalizer | The MultitaskKernel allows Multitask Learning via a modified kernel function |

CMultitaskKernelPlifNormalizer | The MultitaskKernel allows learning a piece-wise linear function (PLIF) via MKL |

CMultitaskKernelTreeNormalizer | The MultitaskKernel allows Multitask Learning via a modified kernel function based on taxonomy |

CNode | A CNode is an element of a CTaxonomy, which is used to describe hierarchical structure between tasks |

CNormDerivativeLem3 | Preprocessor NormDerivativeLem3, performs the normalization used in Lemma3 in Jaakola Hausslers Fischer Kernel paper currently not implemented |

CNormOne | Preprocessor NormOne, normalizes vectors to have norm 1 |

COligoStringKernel | This class offers access to the Oligo Kernel introduced by Meinicke et al. in 2004 |

CCombinedDotFeatures::combined_feature_iterator | |

CPCACut | |

CPerceptron | Class Perceptron implements the standard linear (online) perceptron |

CPerformanceMeasures | Class to implement various performance measures |

CPlif | Class Plif |

CPlifArray | Class PlifArray |

CPlifBase | Class PlifBase |

CPlifMatrix | Store plif arrays for all transitions in the model |

CPluginEstimate | Class PluginEstimate |

CPolyFeatures | Implement DotFeatures for the polynomial kernel |

CPolyKernel | Computes the standard polynomial kernel on CDotFeatures |

CPolyMatchStringKernel | The class PolyMatchStringKernel computes a variant of the polynomial kernel on strings of same length |

CPolyMatchWordStringKernel | The class PolyMatchWordStringKernel computes a variant of the polynomial kernel on word-features |

CPreProc | Class PreProc defines a preprocessor interface |

CPruneVarSubMean | Preprocessor PruneVarSubMean will substract the mean and remove features that have zero variance |

CPyramidChi2 | Pyramid Kernel over Chi2 matched histograms |

CQPBSVMLib | Class QPBSVMLib |

CRealDistance | Class RealDistance |

CRealFileFeatures | The class RealFileFeatures implements a dense double-precision floating point matrix from a file |

CRegulatoryModulesStringKernel | The Regulaty Modules kernel, based on the WD kernel, as published in Schultheiss et al., Bioinformatics (2009) on regulatory sequences |

CRidgeKernelNormalizer | Normalize the kernel by adding a constant term to its diagonal. This aids kernels to become positive definite (even though they are not - often caused by numerical problems) |

CSalzbergWordStringKernel | The SalzbergWordString kernel implements the Salzberg kernel |

CScatterKernelNormalizer | |

CScatterSVM | ScatterSVM - Multiclass SVM |

CSegmentLoss | Class IntronList |

CSerializableAsciiFile | |

CSerializableFile | |

CSet< T > | Template Set class |

CSGObject | Class SGObject is the base class of all shogun objects |

CSigmoidKernel | The standard Sigmoid kernel computed on dense real valued features |

CSignal | Class Signal implements signal handling to e.g. allow ctrl+c to cancel a long running process |

CSignalModel | Class SignalModel |

CSimpleDistance< ST > | Template class SimpleDistance |

CSimpleFeatures< ST > | The class SimpleFeatures implements dense feature matrices |

CSimpleFile< T > | Template class SimpleFile to read and write from files |

CSimpleLocalityImprovedStringKernel | SimpleLocalityImprovedString kernel, is a ``simplified'' and better performing version of the Locality improved kernel |

CSimplePreProc< ST > | Template class SimplePreProc, base class for preprocessors (cf. CPreProc) that apply to CSimpleFeatures (i.e. rectangular dense matrices) |

CSNPFeatures | Features that compute the Weighted Degreee Kernel feature space explicitly |

CSNPStringKernel | The class SNPStringKernel computes a variant of the polynomial kernel on strings of same length |

CSortUlongString | Preprocessor SortUlongString, sorts the indivual strings in ascending order |

CSortWordString | Preprocessor SortWordString, sorts the indivual strings in ascending order |

CSparseDistance< ST > | Template class SparseDistance |

CSparseEuclidianDistance | Class SparseEucldianDistance |

CSparseFeatures< ST > | Template class SparseFeatures implements sparse matrices |

CSparseKernel< ST > | Template class SparseKernel, is the base class of kernels working on sparse features |

CSparsePolyFeatures | Implement DotFeatures for the polynomial kernel |

CSparsePreProc< ST > | Template class SparsePreProc, base class for preprocessors (cf. CPreProc) that apply to CSparseFeatures |

CSparseSpatialSampleStringKernel | Sparse Spatial Sample String Kernel by Pavel Kuksa <pkuksa@cs.rutgers.edu> and Vladimir Pavlovic <vladimir@cs.rutgers.edu> |

CSpectrumMismatchRBFKernel | |

CSpectrumRBFKernel | |

CSqrtDiagKernelNormalizer | SqrtDiagKernelNormalizer divides by the Square Root of the product of the diagonal elements |

CStringDistance< ST > | Template class StringDistance |

CStringFeatures< ST > | Template class StringFeatures implements a list of strings |

CStringFileFeatures< ST > | File based string features |

CStringKernel< ST > | Template class StringKernel, is the base class of all String Kernels |

CStringPreProc< ST > | Template class StringPreProc, base class for preprocessors (cf. CPreProc) that apply to CStringFeatures (i.e. strings of variable length) |

CSubGradientLPM | |

CSubGradientSVM | Class SubGradientSVM |

CSVM | A generic Support Vector Machine Interface |

CSVMLight | |

CSVMLightOneClass | |

CSVMLin | Class SVMLin |

CSVMOcas | Class SVMOcas |

CSVMSGD | Class SVMSGD |

CSVRLight | |

CTanimotoDistance | Class Tanimoto coefficient |

CTanimotoKernelNormalizer | TanimotoKernelNormalizer performs kernel normalization inspired by the Tanimoto coefficient (see http://en.wikipedia.org/wiki/Jaccard_index ) |

CTaxonomy | CTaxonomy is used to describe hierarchical structure between tasks |

CTensorProductPairKernel | Computes the Tensor Product Pair Kernel (TPPK) |

CTime | Class Time that implements a stopwatch based on either cpu time or wall clock time |

CTOPFeatures | The class TOPFeatures implements TOP kernel features obtained from two Hidden Markov models |

CTrainPredMaster | |

CTrie< Trie > | |

CTron | |

CVarianceKernelNormalizer | VarianceKernelNormalizer divides by the ``variance'' |

CWDFeatures | Features that compute the Weighted Degreee Kernel feature space explicitly |

CWDSVMOcas | Class WDSVMOcas |

CWeightedCommWordStringKernel | The WeightedCommWordString kernel may be used to compute the weighted spectrum kernel (i.e. a spectrum kernel for 1 to K-mers, where each k-mer length is weighted by some coefficient ) from strings that have been mapped into unsigned 16bit integers |

CWeightedDegreePositionStringKernel | The Weighted Degree Position String kernel (Weighted Degree kernel with shifts) |

CWeightedDegreeRBFKernel | |

CWeightedDegreeStringKernel | The Weighted Degree String kernel |

CZeroMeanCenterKernelNormalizer | ZeroMeanCenterKernelNormalizer centers the kernel in feature space |

DynArray< T > | Template Dynamic array class that creates an array that can be used like a list or an array |

CExplicitSpecFeatures::explicit_spec_feature_iterator | |

CHashedWDFeatures::hashed_wd_feature_iterator | |

CHashedWDFeaturesTransposed::hashed_wd_transposed_feature_iterator | |

IO | Class IO, used to do input output operations throughout shogun |

joint_list_struct | |

K_THREAD_PARAM< T > | |

libqp_state_T | |

MKLMultiClassGLPK | MKLMultiClassGLPK is a helper class for MKLMultiClass |

MKLMultiClassGradient | MKLMultiClassGradient is a helper class for MKLMultiClass |

MKLMultiClassOptimizationBase | MKLMultiClassOptimizationBase is a helper class for MKLMultiClass |

Model | Class Model |

Parallel | Class Parallel provides helper functions for multithreading |

Parameter | |

CPolyFeatures::poly_feature_iterator | |

segment_loss_struct | Segment loss |

SerializableAsciiReader00 | |

ShogunException | Class ShogunException defines an exception which is thrown whenever an error inside of shogun occurs |

CSimpleFeatures< ST >::simple_feature_iterator | |

CSparseFeatures< ST >::sparse_feature_iterator | |

CSparsePolyFeatures::sparse_poly_feature_iterator | |

SSKDoubleFeature | |

SSKFeatures | |

SSKTripleFeature | |

TParameter | |

CSerializableFile::TSerializableReader | |

TSGDataType | |

TSparse< T > | |

TSparseEntry< T > | |

TString< T > | |

Version | Class Version provides version information |

CWDFeatures::wd_feature_iterator | |

CImplicitWeightedSpecFeatures::wspec_feature_iterator |

SHOGUN Machine Learning Toolbox - Documentation